TY - GEN

T1 - Automatic Aspect Extraction from Scientific Texts

AU - Marshalova, Anna

AU - Bruches, Elena

AU - Batura, Tatiana

N1 - Conference code: 11

PY - 2024

Y1 - 2024

N2 - Being able to extract from scientific papers their main points, key insights, and other important information, referred to here as aspects, might facilitate the process of conducting a scientific literature review. Therefore, the aim of our research is to create a tool for automatic aspect extraction from Russian-language scientific texts of any domain. In this paper, we present a cross-domain dataset of scientific texts in Russian, annotated with such aspects as Task, Contribution, Method, and Conclusion, as well as a baseline algorithm for aspect extraction, based on the multilingual BERT model fine-tuned on our data. We show that there are some differences in aspect representation in different domains, but even though our model was trained on a limited number of scientific domains, it is still able to generalize to new domains, as was proved by cross-domain experiments. The code and the dataset are available at https://github.com/anna-marshalova/automatic-aspect-extraction-from-scientific-texts.

AB - Being able to extract from scientific papers their main points, key insights, and other important information, referred to here as aspects, might facilitate the process of conducting a scientific literature review. Therefore, the aim of our research is to create a tool for automatic aspect extraction from Russian-language scientific texts of any domain. In this paper, we present a cross-domain dataset of scientific texts in Russian, annotated with such aspects as Task, Contribution, Method, and Conclusion, as well as a baseline algorithm for aspect extraction, based on the multilingual BERT model fine-tuned on our data. We show that there are some differences in aspect representation in different domains, but even though our model was trained on a limited number of scientific domains, it is still able to generalize to new domains, as was proved by cross-domain experiments. The code and the dataset are available at https://github.com/anna-marshalova/automatic-aspect-extraction-from-scientific-texts.

KW - Aspect extraction

KW - BERT fine-tuning

KW - Dataset annotation

KW - Scientific information extraction

KW - Sequence labelling

UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85200994417&origin=inward&txGid=778e7a7107844bf260bae80e395454f1

UR - https://www.mendeley.com/catalogue/9cfadbee-b35d-3491-b019-bd8ec9a68342/

U2 - 10.1007/978-3-031-67008-4_6

DO - 10.1007/978-3-031-67008-4_6

M3 - Conference contribution

SN - 9783031670077

T3 - Communications in Computer and Information Science

SP - 67

EP - 80

BT - Communications in Computer and Information Science

PB - Springer Science and Business Media Deutschland GmbH

T2 - 11th International Conference on Analysis of Images, Social Networks and Texts

Y2 - 28 September 2023 through 30 September 2023

ER -